




'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     						=				logging.get_logger(__name__)

lowerCAmelCase_     						=				{'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}

class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Dict						=					'''openai-gpt'''
 __snake_case:							Union[str, Any]						=					{
     '''max_position_embeddings''': '''n_positions''',
     '''hidden_size''': '''n_embd''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }






 def __init__(self       :  Union[str, Any]    ,		a       :  Optional[int]=4_04_78    ,		a       :  str=5_12    ,		a       :  Optional[Any]=7_68    ,		a       :  Union[str, Any]=12    ,		a       :  List[Any]=12    ,		a       :  Dict="gelu"    ,		a       :  List[str]=0.1    ,		a       :  Optional[int]=0.1    ,		a       :  Union[str, Any]=0.1    ,		a       :  Union[str, Any]=1e-5    ,		a       :  Union[str, Any]=0.02    ,		a       :  int="cls_index"    ,		a       :  List[Any]=True    ,		a       :  Optional[int]=None    ,		a       :  str=True    ,		a       :  Union[str, Any]=0.1    ,		**a       :  Union[str, Any]    ,		)       ->   Optional[Any]:






    """simple docstring"""


    A__       =     vocab_size
    A__       =     n_positions
    A__       =     n_embd
    A__       =     n_layer
    A__       =     n_head
    A__       =     afn
    A__       =     resid_pdrop
    A__       =     embd_pdrop
    A__       =     attn_pdrop
    A__       =     layer_norm_epsilon
    A__       =     initializer_range
    A__       =     summary_type
    A__       =     summary_use_proj
    A__       =     summary_activation
    A__       =     summary_first_dropout
    A__       =     summary_proj_to_labels
    super().__init__(**a    )








'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


